16 DevOps Metrics You Should Be Tracking [DORA & Other]

DevOps metrics are quantifiable trackers that provide insights into your DevOps processes’ efficiency, productivity, and overall health. They offer a data-driven approach to understanding your software delivery pipeline, helping teams identify bottlenecks, improve performance, and make informed decisions.

In this blog post, we will explore the importance of monitoring DevOps metrics and what teams should be tracking. From the widely recognized metrics that became mainstream due to DORA (DevOps Research and Assessment) to other essential indicators, we’ll provide a comprehensive guide to help you measure and optimize your DevOps practices.

1. The importance of monitoring DevOps metrics
2. Key DevOps metrics to monitor
3. How to implement effective monitoring practices
4. What tools to use for tracking DORA metrics?

By measuring the right metrics, DevOps teams can gain actionable insights into their workflows, identify problematic areas, and improve their systems over time. Monitoring these metrics isn’t just about collecting data. It’s also about understanding the story behind the numbers and driving more intelligent decision-making. 

This is where selecting and focusing on the right metrics becomes critical.

• Data-driven decision making — DevOps metrics provide objective, quantifiable insights into the performance and health of your systems. They can provide evidence to inform strategic decisions for process improvements, technology investments, and resource allocation.
• Identify inefficiencies and continuous improvement — Monitoring specific metrics around efficiency and frequency can help pinpoint workflow bottlenecks, enabling teams to streamline processes and improve productivity.
  
  The DevOps movement is based on the principle of continuous improvement, and metrics are the foundation of this iterative process. By consistently monitoring and evaluating key metrics, teams can establish a feedback loop to experiment, measure results, and refine their processes over time.
  
  
• Alignment with business goals — DevOps metrics can often help align technical operations, strategy, and broader business objectives.
  
  For example, setting high standards for metrics related to deployment frequency and change lead time can enable an organization to release software faster and respond quickly to market demands.
• Enhancing reliability and quality — Many metrics discussed in this article detect potential issues early. By monitoring these metrics and alerting on them, DevOps teams can spot problems before they escalate to incidents, effectively enhancing the quality of the product’s performance and user satisfaction.
  
  Other metrics, such as the mean time to recovery (MTTR), are critical for assessing a system’s overall reliability and stability.
  
  
• Enhanced collaboration and shared goals — One of the biggest challenges in large and distributed organizations is alignment across different departments and roles. By setting expectations, goals, and KPIs around these DevOps metrics, we can help teams align on shared targets. DevOps metrics serve as a common language for different teams, fostering better collaboration.
  
  
• Benchmarking — Setting and tracking specific and industry-standard DevOps metrics allows organizations to benchmark themselves against their competitors and the industry. This provides an excellent opportunity to measure performance in a quantifiable way and help set ambitious targets. 

Not all metrics are created equal, and in DevOps, knowing which ones to monitor can make the difference between successful monitoring and alert fatigue. Effective DevOps metrics provide actionable insights that drive better decision-making and continuous improvement.

DORA Metrics

The DevOps Research and Assessment (DORA) team has identified four key metrics that are strong indicators of software delivery performance and organizational success. These metrics, known as DORA metrics, are the four key metrics for DevOps success. They provide a comprehensive view of your DevOps practices and are considered industry performance measurement standards. 

Let’s look at the four DORA metrics:

1. Deployment frequency (DF)
2. Lead time (LT)
3. Change failure rate (CFR)
4. Mean time to recovery (MTTR)

1. Deployment frequency (DF)

The deployment frequency metric measures the frequency at which code changes are deployed to production and released to end users. Typically, it is measured as the number of deployments per a predefined period, such as per day. 

Higher deployment frequency indicates a team’s ability to deliver small batches of work quickly, reducing risk and increasing agility. It also directly indicates how quickly a team can deliver value to end customers.

2. Lead time (LT)

Lead time to changes measures the time it takes for a code commit to become production-ready. Shorter lead times demonstrate that a team can quickly adapt to changing requirements and deliver value rapidly. Long lead times often indicate inefficiencies in the development or testing process, slowing down the entire delivery pipeline. 

The lead time metric can be calculated using the time of code commits and the start of the releases. 

3. Change failure rate (CFR)

The change failure rate metric measures the percentage of deployments that result in a failure requiring a rollback or remediation. A lower change failure rate indicates higher quality and more stable releases, reducing downtime and improving customer satisfaction.

4. Mean time to recovery (MTTR)

Mean time to recovery measures the average time it takes to recover from a production failure. Failures and downtime are part of the game, but what sets high-performing organizations apart is how fast they can detect and resolve issues. 

Low MTTR and faster recovery times minimize the impact of failures on users and demonstrate a team’s ability to respond to and resolve problems quickly.

Additional DevOps metrics and KPIs

Apart from the solid foundation you get from tracking and reporting on the DORA metrics, several other metrics offer valuable insights into various aspects of the software delivery lifecycle, the system’s health and reliability, and operational efficiency. 

5. SLAs & SLOs

Service level agreements (SLAs) and service level objectives (SLOs) define the expected service reliability and performance level. 

SLAs are usually contractual agreements defining the expected level of service. SLOs are specific, measurable targets that you set for service performance. 

Meeting SLAs and SLOs is crucial for maintaining customer trust and fulfilling contractual obligations. Frequent violations may indicate underlying issues with system reliability or capacity planning.

6. Availability/Uptime

Availability and uptime metrics measure the percentage of time that a system or a service is operational and functional to end users. High availability correlates with reliable systems, and monitoring uptime helps teams identify and address issues that lead to downtime.

7. Application usage and traffic

Application usage and traffic metrics generally measure the number of users, requests, transactions, or other relevant volume-based metrics that a system or an application handles over time. These metrics provide insights into system scalability and capacity by measuring system load. 

They are also used to optimize resource allocation and identify popular features. Typical metrics include daily/monthly active users (DAU/MAU), page views, and API calls.  

8. Application performance

Application performance metrics measure how well an application functions from a user’s perspective. 

Typical metrics include:

• Response time
• Error rates
• Latency
• Throughput

Monitoring performance metrics with tools such as Application Performance Monitoring (APM) ensures the app meets user expectations and helps avoid churn.

9. Test coverage

Test coverage metric measures the percentage of software code covered by automated tests. Higher test coverage reduces the risk of undetected bugs reaching production systems. Examples of tests include unit tests, integration tests, and end-to-end tests. Aim for high coverage, but your tests must be more meaningful, as maintaining them is also a cost. 

10. Infrastructure as code (IaC) coverage

Infrastructure as code (IaC) coverage measures the percentage of infrastructure resources managed through code rather than manual configuration. 

Higher IaC coverage correlates with improved consistency, repeatability, and effective disaster recovery capabilities aligning with DevOps best practices. 

11. Defect escape rate

Defect escape rate measures the percentage of defects that make it to production systems versus those caught during testing. A high defect escape rate might indicate testing and quality assurance gaps.

12. Mean time to detection (MTTD)

The mean time to detection metric measures the average time it takes to identify an issue or incident after it occurs. The faster we can detect and identify problems, the faster we can resolve them. MTTD reflects the effectiveness of an organization’s observability and alerting strategy.

13. Mean time to failure (MTTF)

Mean time to failure measures the average time between system failures or critical production incidents that disrupt a system’s operational function. In general, longer MTTF indicates higher system reliability.

14. Continuous Integration (CI) metrics (runs, average time, failure rate)

These metrics around CI measure performance and efficiency. They typically include the number of CI runs, the average time per run, and the failure rate of builds. Efficient CI pipelines reduce developer friction and idle time and enable faster feedback loops. High failure rates or long build times indicate areas for improvement. 

15. Vulnerability open rate (VOR)

The vulnerability open rate tracks the rate at which new security vulnerabilities are discovered in your systems. This metric is usually measured when new code is released into production. Tracking VOR helps teams prioritize security and minimize risk by addressing vulnerabilities before they are exploited.

16. Unit cost

Unit cost tracks the cost of delivering a unit of service or functionality relevant to your business. Measuring total or aggregate costs is often not a good measurement of cost-effectiveness.

Measuring and reporting unit costs provides a more accurate representation of how efficient you are with your resources as it directly relates to a relevant business metric. Examples include cost per transaction, cost per user, cost per widget, and cost per feature.  

The real value of an effective observability strategy is how you monitor and act on the measured metrics, driving continuous improvements and operational excellence.

• Choose the right metrics — Not all metrics will be equally relevant to every team or organization. The key is identifying which metrics align with your goals, whether they focus on speed, quality, reliability, or cost efficiency. 
• Define clear objectives and benchmarks — Define the reasoning behind measuring these metrics and tie it back to a business or operational goal. Clearly defined objectives ensure your monitoring efforts are purposeful and impactful. Monitoring is most effective when you can measure progress against a baseline. Use historical data to establish benchmarks for your metrics.
• Choose the right tools — Select monitoring tools that align with your organization’s needs and scale. Before you select any tools, list all your needs and requirements and perform trial periods with various tools to test them out.
  
  Ensure these tools integrate with your existing workflows and provide real-time actionable insights while fitting your budget. Use these tools to automate your metrics’ collection, aggregation, and analysis.
• Set meaningful alerts — Leverage tools with built-in alerting and anomaly detection to ensure your team is notified of issues as they arise. Too many alerts can overwhelm teams, leading to alert fatigue. Configure meaningful alerts that focus on critical thresholds and prioritize alerts that require immediate action. 
• Automate remediation where possible — In some instances, automatic remediation of alerts and issues might be possible. Invest time to automatically fix issues whenever feasible with functionalities such as autoscaling, rolling back deployments, or self-healing automation. This reduces the need for human intervention, freeing teams to focus on more complex problems.
• Continuously review and refine — Monitoring isn’t a set-it-and-forget-it process. Regularly review your observability strategy to ensure it aligns with evolving goals and challenges. Drop metrics that no longer add value and introduce new ones as needed to address emerging priorities. Conduct post-incident reviews to understand what happened and how to prevent similar issues. 

Tools for tracking DORA metrics offer diverse features, such as integration capabilities, ease of use, and specific focus areas within the software development lifecycle. 

Below is a summary of some of the top tools:

A successful DevOps implementation requires capable tools that automate your processes, including CI/CD, IaC, and infrastructure management. These practices can be tricky to get right, but dedicated platforms make it easy to control your infrastructure resources — saving valuable time that can be returned to your business.

Spacelift is an IaC management platform that helps you implement DevOps best practices. Spacelift provides a dependable CI/CD layer for infrastructure tools including OpenTofu, Terraform, Pulumi, Kubernetes, Ansible, and more, letting you automate your IaC delivery workflows.

Spacelift is designed for your whole team. Everyone works in the same space, supported by robust policies that enforce access controls, security guardrails, and compliance standards. You can manage your DevOps infrastructure much more efficiently, without compromising on safety.

With Spacelift, you get:

• Policies to control what kind of resources engineers can create, what parameters they can have, how many approvals you need for a run, what kind of task you execute, what happens when a pull request is open, and where to send your notifications
• Stack dependencies to build multi-infrastructure automation workflows with dependencies, having the ability to build a workflow that, for example, generates your EC2 instances using Terraform and combines it with Ansible to configure them
• Self-service infrastructure via Blueprints, or Spacelift’s Kubernetes operator, enabling your developers to do what matters – developing application code while not sacrificing control
• Creature comforts such as contexts (reusable containers for your environment variables, files, and hooks), and the ability to run arbitrary code
• Drift detection and optional remediation

DevOps metrics are essential for understanding and optimizing your organizational processes around software development and delivery. By monitoring the right metrics, teams can identify inefficiencies, enhance system reliability, and foster a culture of continuous improvement. In this blog post, we analyzed all the various actionable metrics you should be monitoring and discussed how to implement effective monitoring and observability practices.  

The real power of metrics lies not just in tracking them but also in using them to drive actionable insights and improvements. Whether you’re new to DevOps or looking to refine your existing processes, identifying and monitoring key DevOps metrics is necessary to build resilient, efficient, and high-performing systems.